Clinical Cancer Advances 2018: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A MESSAGE FROM ASCO'S PRESIDENT I remember when ASCO first conceived of publishing an annual report on the most transformative research occurring in cancer care. Thirteen reports later, the progress we have chronicled is remarkable, and this year is no different. The research featured in ASCO's Clinical Cancer Advances 2018 report underscores the impressive gains in our understanding of cancer and in our ability to tailor treatments to tumors' genetic makeup. The ASCO 2018 Advance of the Year, adoptive cell immunotherapy, allows clinicians to genetically reprogram patients' own immune cells to find and attack cancer cells throughout the body. Chimeric antigen receptor (CAR) T-cell therapy-a type of adoptive cell immunotherapy-has led to remarkable results in young patients with acute lymphoblastic leukemia (ALL) and in adults with lymphoma and multiple myeloma. Researchers are also exploring this approach in other types of cancer. This advance would not be possible without robust federal investment in cancer research. The first clinical trial of CAR T-cell therapy in children with ALL was funded, in part, by grants from the National Cancer Institute (NCI), and researchers at the NCI Center for Cancer Research were the first to report on possible CAR T-cell therapy for multiple myeloma. These discoveries follow decades of prior research on immunology and cancer biology, much of which was supported by federal dollars. In fact, many advances that are highlighted in the 2018 Clinical Cancer Advances report were made possible thanks to our nation's support for biomedical research. Funding from the US National Institutes of Health and the NCI helps researchers pursue critical patient care questions and addresses vital, unmet needs that private industry has little incentive to take on. Federally supported cancer research generates the biomedical innovations that fuel the development and availability of new and improved treatments for patients. We need sustained federal research investment to accelerate the discovery of the next generation of cancer treatments. Another major trend in this year's report is progress in precision medicine approaches to treat cancer. Although precision medicine offers promise to people with cancer and their families, that promise is only as good as our ability to make these treatments available to all patients. My presidential theme, "Delivering Discoveries: Expanding the Reach of Precision Medicine," focuses on tackling this formidable challenge so that new targeted therapies are accessible to anyone who faces a cancer diagnosis. By improving access to high-quality care, harnessing big data on patient outcomes from across the globe, and pursuing innovative clinical trials, I am optimistic that we will speed the delivery of these most promising treatments to more patients. Sincerely, Bruce E. Johnson, FASCO ASCO President, 2017 to 2018.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it